OpenAI’s ChatGPT Enterprise has “many, many, many thousands” of companies waiting



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OpenAI’s ChatGPT Enterprise is in high demand, as COO Brad Lightcap reveals in a CNBC interview.

According to Lightcap, “many, many, many thousands” of companies are on the waiting list for the AI tool. Since November, 92 percent of Fortune 500 companies have used ChatGPT, a significant increase from 80 percent in August.

Lightcap suspects that the remaining eight percent are heavy industry, capital-intensive oil and gas companies, or industries with a lot of heavy machinery in general, where the focus is on producing goods rather than information and service tasks.

ChatGPT Enterprise: Rapid growth and high-profile customers

ChatGPT Enterprise launched in August with 20 companies as beta testers, including Block and Canva. Lightcap says the last two months have been focused on making sure early customers are getting value from the product.

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OpenAI plans to offer ChatGPT Enterprise as a self-service option soon. Next year, all of the thousands of interested companies on the waiting list will be served, according to Lightcap.

ChatGPT Enterprise benefits
ChatGPT Enterprise benefits | Bild: OpenAI (Screenshot)

Lightcap says AI is being used primarily in technical fields, where ChatGPT acts as a research assistant, sidekick, or technical assistant. The tool assists professionals in fields such as software development, mechanical engineering, chemistry, and biology.

Generative AI is overrated and underrated

According to Lightcap, OpenAI almost always focuses on value rather than revenue when developing products. The company’s goal is to create useful tools like ChatGPT that cover a wide range of applications.

When asked which aspects of AI are overestimated and which are underestimated, Lightcap says that the immediate impact of AI in terms of significant business change is overestimated.

AI systems are still at an early stage of development, and there is no one-size-fits-all solution for complex business problems. The transition must be individual and gradual, with a high rate of trial and error in the early implementation phase.

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